Will smart automation, intelligent software bots and brainy robots take away our jobs anytime soon?
Pose this question to any Indian working in a company where unions are strong, or to any Indian who has a government job, or to the majority of Indians who work in the unorganized sector—those who drive taxis, trucks pull handcarts, hawk goods on footpaths or are employed as maids—and you will, in all probability, be looked at askance or even dismissed as an uninformed prophet of doom.
The reaction may not be surprising in emerging countries like India, given that a majority of such employees would never have heard about the Industrial Revolution, or terms like disguised unemployment, cloud computing, machine learning, deep learning, automation or artificial intelligence (AI)-driven software bots.
They would perhaps have also never heard of drones taking photographs and doing surveillance; of robots delivering pizzas and packages; of assistive robots taking care of the elderly; of robots making hamburgers and others like the Roomba robots that mop floors; of software bots writing articles and movie scripts; of three-dimensional or 3D printing revolutionizing the manufacturing sector; of driverless cars and trucks--all of which would make it very hard for them to imagine the future impact of these technologies that have not yet directly touched their lives or their jobs.
Yes. They would have surely seen humanoid robots in sci-fi films like actor Rajnikant’s Enthiran in Tamil or Robot in English, or a movie like Terminator or Transformers. But even these robots would appear keen on subjugating the human race (http://bit.ly/29fgF06)—an issue that is beyond the scope of this article (http://bit.ly/29qDiOf)—rather than interested in snatching their jobs.
However, were you to ask engineers employed in the Indian information technology (IT) services sector the same question—whether they fear losing their jobs to automation and AI-infused bots—their answer would underscore the dichotomy that exists in the Indian workforce.
The reason is simple: employees in the IT services sector are already seeing the first signs of losing their jobs to intelligent automation.
Consider this. For the first time in over two decades, two of India’s five largest software services firms, Wipro Ltd and HCL Technologies Ltd, reported a net decline in direct hiring (http://bit.ly/1Vl8cc8) in the January-March quarter of 2016, following rapid adoption of newer technologies such as cloud computing and automation platforms.
Wipro, for instance, has an artificial intelligence (AI) platform called Holmes that can automate several aspects of the so-called fixed-price projects, saving up to $46.5 million and freeing around 3,000 engineers from mundane software maintenance activities (http://bit.ly/1suHEK5). Similarly, Tata Consultancy Services Ltd (TCS) and Infosys Ltd are also relying on their intelligent platforms, Ignio and Mana, respectively, to improve their profitability and revenue per employee.
US-based IPsoft Inc., which uses artificial intelligence (AI) to manage computer networks, has forged a partnership with Accenture Plc whereby the latter will use Amelia internally and also look to sell the bot to its clients. IPsoft, incidentally, plans to launch its third cognitive computing platform Apollo soon (http://bit.ly/29hmE3Q).
It was this very sector that historically prided itself on being a major direct hirer of engineers besides creating millions of ancillary jobs but it is evident that this will no longer be the case.
India is set to lose 640,000 low-skilled positions to automation by 2021, according to a 3 July report by US-based research firm, HorsesforSources (HfS). Low-skilled workers conduct simple entry level, process driven tasks that require little abstract thinking or autonomy, according to HfS. Medium-to-high level workers undertake more complicated tasks that require experience, complex problem solving, ability to learn on-the-job and to work autonomously.
The job losses are taking place due to the Robotics Process Automation (RPA)—an automation-led service delivery model that enables cost-effective automation of basic rule-based tasks across client functions, replacing low-value processes performed by human FTEs (full-time employee) with virtual or robot FTEs operating robot applications or software robot.
Such AI and automation platforms operate throughout the day and night at about one-third of the cost of similar off-shore operators or about one-ninth the cost of an onshore operator performing similar roles, according to a September 2015 report by Australia-based advisory firm Mindfields Consulting.
What’s so new this time around?
Automation is not new to factories. If the Industrial Revolution introduced the assembly line production concept in factories, the 50s and 60s saw companies like General Motors introduce robotics on shop floors. It’s the acceleration in capabilities of software automation, machine learning, deep learning, AI and predictive algorithms.
A machine learning system, for instance, does not need programming and can teach itself from mountains of data. It is a subset of AI, which itself is a collection of technologies and concepts.
Deep learning, which uses artificial neural networks (ANNs) that are loosely modelled on the human brain, can be said to be a variant or even a subset of machine learning. Deep learning, thus, takes machine learning closer to AI.
Popular machine learning applications include Google’s self-driving car, online recommendations from e-commerce companies such as Amazon.com and Flipkart.com, dating site Tinder.com and streaming video site Netflix.com. Credit scoring and offers are based on machine learning applications and so are new pricing models, email filtering and pattern and image recognition.
Machine learning is already helping doctors make better diagnoses. Advanced cyber-defence systems use machine learning that mimic the human immune system to learn autonomously, adapt to changes in corporate technology or its users, and spot nefarious activities. Ironically, it’s these very capabilities that are making machines smarter than human beings.
By 2020, autonomous software agents outside of human control will participate in 5% of all economic transactions, according to a 7 October 2015, note by research firm Gartner Inc. An algorithm, in the simplest form, is a step-by-step procedure to execute certain action. When written in software code that is understood by machines, it can be turned into a tool to control and manipulate machines.
Algorithmically-driven agents are already participating in our economy. However, while these agents are automated, they are not fully autonomous. New autonomous software agents will hold value themselves, and function as the fundamental underpinning of a new economic paradigm that Gartner calls the “programmable economy" or “algorithmic economy".
For instance, Amazon’s recommendation algorithm keeps customers engaged and buying, while Netflix’s dynamic algorithm that is built through crowdsourcing, keeps people busy with binge watching. Google-owned Waze’s algorithm directs thousands of independent cars on the road. E-commerce companies, banks, financial institutions, retailers and oil and gas companies in India are already heavy users of algorithm-based business decisions. Stock markets the world over have made trading very fast-paced with algorithms, thus giving rise to robo-advisory services to keep pace with the trend.
Gartner believes that by 2018, more than 3 million workers globally will be supervised by a “robo boss" who will increasingly make decisions that previously could only have been made by a human manager. In the same period, the research firm believes that 45% of the fastest-growing firms will have fewer employees than smart machines.
The 7 October report cites possible examples of a fully automated supermarket or a security firm offering drone-only surveillance services. The report also predicts that by 2018-end, customer digital assistants will recognize individuals by face and voice across channels and partners, and by 2020, smart agents will facilitate 40% of mobile interactions, and the post-app era will begin to dominate.
Gartner also predicts that by 2018, 20% of business content will be authored by machines. Technologies with the ability to proactively assemble and deliver information through automated composition engines are fostering a movement from human- to machine-generated business content. Data-based and analytical information can be turned into natural language writing using these emerging tools. Business content, such as shareholder reports, legal documents, market reports, press releases and white papers, are all candidates for automated writing, it notes.
Accenture’s 2015 Tech Vision has a similar view. It notes that while advances in natural interfaces, wearable devices and smart machines are presenting new opportunities for firms to empower their workers through technology, it will also raise new challenges in managing a collaborative workforce of people and machines.
Routine jobs most at stake
In his book The Rise of the Robots: Technology and the Threat of Mass Employment, Martin Ford contends that while all jobs are at risk of automation, it is the “routine" and “predictable" jobs that will be impacted most. For instance, it’s not hard to imagine that many drivers would be rendered redundant if driverless cars and trucks eventually go mainstream. Similarly, AI- and drone-driven surveillance systems can drastically reduce the number of security guards. Of course, they will raise questions of privacy, which policy makers will have to take cognizance of.
Ford cites a 2013 study by the University of Oxford’s Martin School of over 700 job types and concluded that nearly 50% of US jobs will ultimately be susceptible to full machine automation. Ford contends that robotics and advanced self-service technologies will primarily threaten lower-wage jobs that require modest levels of education and training. Automated vehicles or construction-scale 3D printers may eventually destroy millions of jobs.
Engineers at a Silicon Valley start-up Industrial Perception Inc., for instance, believe that their robots will ultimately be able to move a box every second as compared to a human worker who needs around six seconds to complete the same task. Tesla’s new factory in Fremont, California, uses 160 highly flexible industrial robots to assemble about 400 cars a day.
Boston-based Rethink Robotics’ robot called Baxter costs significantly less than a year’s wages for a typical US manufacturing worker. These robots have been built on the free and open source Robot Operating System (ROS), which was originally conceived at Stanford University’s Artificial Intelligence (AI) Laboratory.
Momentum Machines Inc.’s device is capable of producing about 360 hamburgers per hours, also toasts the bun and then slices and adds fresh ingredients like tomatoes, onions, and pickles only after the order is placed. The company’s co-founder, as quoted in Ford’s book, states categorically that “Our device isn’t meant to make employees more efficient...It’s meant to completely obviate them."
Robots at Japan’s Kura sushi 262 restaurant chains already make the sushi while conveyor belts replace waiters—a move that has helped the company price its sushi plates at just about $1. Amazon and Kroger are examples of automation in retail outlets. Imagine if companies like McDonald’s, with about 1.8 million workers worldwide, were to employ such a device, which Momentum Machines believes “will pay for itself in less than a year".
Foxconn, a key manufacturing partner for Apple Inc., Google Inc., and Amazon.com Inc., already uses about 60,000 robots while Wal-Mart Stores, Inc., the third-largest global employer with 2.1 million workers, wants to replace its warehouse stock-checkers with flying drones that can scan miles of shelves in a fraction of the time, according to a 3 June article on the World Economic Forum (WEF) website (http://bit.ly/28ExIyn).
Even media jobs are being threatened. ‘Stats Monkey’, a software developed by students and researchers at Northwestern University’s Intelligent Information Laboratory is designed to automate sports reporting by transforming objective data about a particular game into a compelling narrative. In 2010, the researchers formed a company, Narrative Science, and went a step further and developed ‘Quill’ that produces automated articles in areas including sports, business and politics. Moreover, even symphonies and movies are now being written by AI algorithms.
Klaus Schwab, WEF founder and executive chairman, calls this trend the “Fourth Industrial Revolution". He wrote on 14 January (http://bit.ly/1pBfye4) that “we stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another".
According to Schwab, while the First Industrial Revolution used water and steam power to mechanize production, the Second one leveraged electric power to create mass production. The Third Industrial Revolution employed electronics and information technology to automate production. Now, a Fourth Industrial Revolution is building on the Third: the digital revolution that has been occurring since the middle of the last century and is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres.
Schwab is optimistic that this revolution has the potential to raise global income levels and improve the quality of life for populations around the world. Ordering a cab, booking a flight, buying a product, making a payment, listening to music, watching a film, or playing a game—any of these can now be done remotely, he points out.
Schwab does acknowledge that economists such as Erik Brynjolfsson and Andrew McAfee have pointed out that this digital revolution could also yield greater inequality, particularly in its potential to disrupt labour markets. As automation substitutes labour across the entire economy, the net displacement of workers by machines might exacerbate the gap between returns to capital and returns to labour. But he argues that “...we cannot foresee at this point which scenario is likely to emerge, and history suggests that the outcome is likely to be some combination of the two," Schwab argues in his article.
So are human-machine collaboration jobs one way to address the issue, as some economists point out? Ford argues that such jobs will certainly exist but likely to be relatively few in number and often, short-lived. In a great many cases, they may also be unrewarding or even dehumanizing.
Ford’s contention is that when a worker is replaced by a machine, that machine does not go out and consume even though it does use energy, spare parts and requires maintenance. This trend could affect the prospects of a modern mass-market economy. Moreover, weak demand could unleash a secondary wave of job losses, affecting even those occupations not directly susceptible to automation.
There are robots that move boxes, make hamburgers, algorithms that create music, write reports and even trade on the stock exchange. But these employ what is categorized as specialised or ‘narrow’ artificial intelligence—something that even IBM Watson falls under, according to Ford.
But firms are working towards building a genuinely intelligent machine that can conceive new ideas, demonstrate an awareness of its own existence and carry on coherent conversations—the Holy Grail of AI.
This, according to futurist and inventor Ray Kurzweil, would usher in an event, or perhaps an era, called ‘The Singularity’. Ray Kurzweil predicted that we will inevitably merge with the machines of the future and humans will be augmented with brain implants (aka Matrix) that dramatically enhance intelligence. There are many critics of this view too.
Some professions with low unemployment rates will include those especially healthcare-related fields like nursing, and any job that combines dexterity, mobility, creativity and human interaction—maybe plumbers or electricians.
In most areas, no amount of education or training—even from the most elite universities—would make a human being competitive with such machines. This implies that income from capital would be concentrated in the hands of a tiny elite, says Ford. If machines substitute workers entirely, then no one will have a job or income from any type of labour. This would imply no purchasing power, which is bound to affect economic growth. Even the tiny elite simply cannot continuously purchase goods and services to keep the global economy growing.
However, there are those like Robert D. Atkinson, founder and president of the Information Technology and Innovation Foundation (ITIF), who decry the scaremongering that AI has generated. In a 6 June report, Atkinson pointed out that although AI has become commonplace—most smartphones contain some version of AI, such as speech recognition—the public still has a poor understanding of the technology.
What can be done, or what needs to be done? According to HfS chief executive and chief analyst, Phil Fersht, India needs to focus on new avenues for services job creation where it has strength in numbers and strength in potential. “Engineering services in a bright spot, and so is analytics, with their being such a proficiency for data and technology from its services talent. Moreover, India has a very strong competency for process and, believe it or not, automation capability. So why not become a leader in helping clients access better data from better automated processes?"
Ford, though, counters that even the “freed up" workers will not necessarily be absorbed in newer jobs. So is there a solution? The most effective solution, say some experts, is likely to be some form of basic income guarantee.
Other experts do not take such a grim view. While algorithms and hardware capability are improving rapidly, “we don’t have anything to worry about machines taking over in the near future", Matthew Grob, executive vice president and chief technology officer at US wireless technologies company Qualcomm, said at the World Economic Forum Annual Meeting 2016 this February.
The current trends, according to a January report on the ‘Future of Jobs’ by the World Economic Forum (WEF) predicts that the current trend could lead to a net employment impact of more than 5.1 million jobs lost to disruptive labour market changes over the period 2015–2020, with a total loss of 7.1 million jobs—two thirds of which are concentrated in routine white collar office functions, such as office and administrative roles—and a total gain of 2 million jobs, in computer and mathematical and architecture and engineering-related fields.
Manufacturing and production roles are also expected to see a further bottoming out but are also anticipated to have relatively good potential for upskilling, re-deployment and productivity enhancement through technology rather than pure substitution, the WEF report says.
Two job types stand out due to the frequency and consistency with which they were mentioned across practically all industries and geographies, the report points out.
The first are data analysts, which companies expect will help them make sense and derive insights from the torrent of data generated by technological disruptions. The second are specialized sales representatives, as practically every industry will need to become skilled in commercializing and explaining their offerings to business or government clients and consumers, either due to the innovative technical nature of the products themselves or due to new client targets with which the company is not yet familiar, or both.
For a talent revolution to take place, the WEF report recommends, governments and businesses will need to profoundly change their approach to education, skills and employment, and their approach to working with each other.
Fersht of HfS asserts that the future (http://bit.ly/1nwTErw) is “more about the type of jobs we need to create, not the ones we could protect". He is confident that the “new generation of kids coming out of college is not clamouring to process insurance claims, sit on IT help-desks, input data into payroll systems or manage customer orders".
He concludes: “We are now experiencing the aftershock of the shift towards the As-a-Service Economy. So let’s stop trying to peer blindly into an uncertain future, and instead address an exciting present where there is real potential to achieve new thresholds of business value."